Recovery mechanisms of Arctic summer sea ice

We examine the recovery of Arctic sea ice from prescribed ice-free summer conditions in simulations of 21st century climate in an atmosphere–ocean general circulation model. We find that ice extent recovers typically within two years. The excess oceanic heat that had built up during the ice-free summer is rapidly returned to the atmosphere during the following autumn and winter, and then leaves the Arctic partly through increased longwave emission at the top of the atmosphere and partly through reduced atmospheric heat advection from lower latitudes. Oceanic heat transport does not contribute significantly to the loss of the excess heat. Our results suggest that anomalous loss of Arctic sea ice during a single summer is reversible, as the ice–albedo feedback is alleviated by large-scale recovery mechanisms. Hence, hysteretic threshold behavior (or a “tipping point”) is unlikely to occur during the decline of Arctic summer sea-ice cover in the 21st century.

The transient versus the equilibrium response of sea ice to global warming

To examine the long-term stability of Arctic and Antarctic sea ice, idealized simulations are carried out with the climate model ECHAM5/MPIOM. Atmospheric CO2 concentration is increased over 2000 years from pre-industrial levels to quadrupling, is then kept constant for 5940 years, is afterwards decreased over 2000 years to pre-industrial levels, and finally kept constant for 3940 years.Despite these very slow changes, the sea-ice response significantly lags behind the CO2 concentration change. This lag, which is caused by the ocean’s thermal inertia, implies that the sea-ice equilibrium response to increasing CO2 concentration is substantially underestimated by transient simulations. The sea-ice response to CO2 concentration change is not truly hysteretic and in principle reversible.We find no lag in the evolution of Arctic sea ice relative to changes in annual-mean northern-hemisphere surface temperature. The summer sea-ice cover changes linearly with respect to both CO2 concentration and temper...

Predictability of large interannual Arctic sea-ice anomalies

In projections of twenty-first century climate, Arctic sea ice declines and at the same time exhibits strong interannual anomalies. Here, we investigate the potential to predict these strong sea-ice anomalies under a perfect-model assumption, using the Max-Planck-Institute Earth System Model in the same setup as in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We study two cases of strong negative sea-ice anomalies: a 5-year-long anomaly for present-day conditions, and a 10-year-long anomaly for conditions projected for the middle of the twenty-first century. We treat these anomalies in the CMIP5 projections as the truth, and use exactly the same model configuration for predictions of this synthetic truth. We start ensemble predictions at different times during the anomalies, considering lagged-perfect and sea-ice-assimilated initial conditions. We find that the onset and amplitude of the interannual anomalies are not predictable. However, the further deepening of the anomaly can be predicted for typically 1 year lead time if predictions start after the onset but before the maximal amplitude of the anomaly. The magnitude of an extremely low summer sea-ice minimum is hard to predict: the skill of the prediction ensemble is not better than a damped-persistence forecast for lead times of more than a few months, and is not better than a climatology forecast for lead times of two or more years. Predictions of the present-day anomaly are more skillful than predictions of the mid-century anomaly. Predictions using sea-ice-assimilated initial conditions are competitive with those using lagged-perfect initial conditions for lead times of a year or less, but yield degraded skill for longer lead times. The results presented here suggest that there is limited prospect of predicting the large interannual sea-ice anomalies expected to occur throughout the twenty-first century.

Seasonal to interannual Arctic sea-ice predictability in current GCMs

We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

The impact of sea ice dynamics on the Arctic climate system

Five paired global climate model experiments, one with an ice pack that only responds thermodynamically (TI) and one including sea-ice dynamics (DI), were used to investigate the sensitivity of Arctic climates to sea-ice motion. The sequence of experiments includes situations in which the Arctic was both considerably colder (Glacial Inception, ca 115,000 years ago) and considerably warmer (3 × CO2) than today. Sea-ice motion produces cooler anomalies year-round than simulations without ice dynamics, resulting in reduced Arctic warming in warm scenarios and increased Arctic cooling in cold scenarios. These changes reflect changes in atmospheric circulation patterns: the DI simulations favor outflow of Arctic air and sea ice into the North Atlantic by promoting cyclonic circulation centered over northern Eurasia, whereas the TI simulations favor southerly inflow of much warmer air from the North Atlantic by promoting cyclonic circulation centered over Greenland. The differences between the paired simulations are sufficiently large to produce different vegetation cover over >19% of the land area north of 55°N, resulting in changes in land-surface characteristics large enough to have an additional impact on climate. Comparison of the DI and TI experiments for the mid-Holocene (6000 years ago) with paleovegetation reconstructions suggests the incorporation of sea-ice dynamics yields a more realistic simulation of high-latitude climates. The spatial pattern of sea-ice anomalies in the warmer-than-modern DI experiments strongly resembles the observed Arctic Ocean sea-ice dipole structure in recent decades, consistent with the idea that greenhouse warming is already impacting the high-northern latitudes.

Sea surface temperature climate change initiative: alternative image classification algorithms for sea-ice affected oceans

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.

September Arctic sea-ice minimum predicted by spring melt-pond fraction

The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2, 3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5, 6, 7, 8, 9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.

Impact of variable atmospheric and oceanic form drag on simulations of Arctic sea ice

Over Arctic sea ice, pressure ridges and floe andmelt pond edges all introduce discrete obstructions to the flow of air or water past the ice and are a source of form drag. In current climate models form drag is only accounted for by tuning the air–ice and ice–ocean drag coefficients, that is, by effectively altering the roughness length in a surface drag parameterization. The existing approach of the skin drag parameter tuning is poorly constrained by observations and fails to describe correctly the physics associated with the air–ice and ocean–ice drag. Here, the authors combine recent theoretical developments to deduce the total neutral form drag coefficients from properties of the ice cover such as ice concentration, vertical extent and area of the ridges, freeboard and floe draft, and the size of floes and melt ponds. The drag coefficients are incorporated into the Los Alamos Sea Ice Model (CICE) and show the influence of the new drag parameterization on the motion and state of the ice cover, with the most noticeable being a depletion of sea ice over the west boundary of the Arctic Ocean and over the Beaufort Sea. The new parameterization allows the drag coefficients to be coupled to the sea ice state and therefore to evolve spatially and temporally. It is found that the range of values predicted for the drag coefficients agree with the range of values measured in several regions of the Arctic. Finally, the implications of the new form drag formulation for the spinup or spindown of the Arctic Ocean are discussed.

A review on Arctic sea ice predictability and prediction on seasonal-to-decadal timescales

Sea ice plays a crucial role in the earth's energy and water budget and substantially impacts local and remote atmospheric and oceanic circulations. Predictions of Arctic sea ice conditions a few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea ice predictability on these timescales. Predictability mainly originates from persistence or advection of sea ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state-of-the-art models, current sea ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea ice forecasting are presented, along with suggestions for future research priorities.

Pan-Arctic and regional sea ice predictability: initialization month dependence

Seasonal-to-interannual predictions of Arctic sea ice may be important for Arctic communities and industries alike. Previous studies have suggested that Arctic sea ice is potentially predictable but that the skill of predictions of the September extent minimum, initialized in early summer, may be low. The authors demonstrate that a melt season “predictability barrier” and two predictability reemergence mechanisms, suggested by a previous study, are robust features of five global climate models. Analysis of idealized predictions with one of these models [Hadley Centre Global Environment Model, version 1.2 (HadGEM1.2)], initialized in January, May and July, demonstrates that this predictability barrier exists in initialized forecasts as well. As a result, the skill of sea ice extent and volume forecasts are strongly start date dependent and those that are initialized in May lose skill much faster than those initialized in January or July. Thus, in an operational setting, initializing predictions of extent and volume in July has strong advantages for the prediction of the September minimum when compared to predictions initialized in May. Furthermore, a regional analysis of sea ice predictability indicates that extent is predictable for longer in the seasonal ice zones of the North Atlantic and North Pacific than in the regions dominated by perennial ice in the central Arctic and marginal seas. In a number of the Eurasian shelf seas, which are important for Arctic shipping, only the forecasts initialized in July have continuous skill during the first summer. In contrast, predictability of ice volume persists for over 2 yr in the central Arctic but less in other regions.

The response of the sea ice edge to atmospheric and oceanic jet formation

The sea ice edge presents a region of many feedback processes between the atmosphere, ocean, and sea ice (Maslowski et al.). Here the authors focus on the impact of on-ice atmospheric and oceanic flows at the sea ice edge. Mesoscale jet formation due to the Coriolis effect is well understood over sharp changes in surface roughness such as coastlines (Hunt et al.). This sharp change in surface roughness is experienced by the atmosphere and ocean encountering a compacted sea ice edge. This paper presents a study of a dynamic sea ice edge responding to prescribed atmospheric and oceanic jet formation. An idealized analytical model of sea ice drift is developed and compared to a sea ice climate model [the Los Alamos Sea Ice Model (CICE)] run on an idealized domain. The response of the CICE model to jet formation is tested at various resolutions. It is found that the formation of atmospheric jets at the sea ice edge increases the wind speed parallel to the sea ice edge and results in the formation of a sea ice drift jet in agreement with an observed sea ice drift jet (Johannessen et al.). The increase in ice drift speed is dependent upon the angle between the ice edge and wind and results in up to a 40% increase in ice transport along the sea ice edge. The possibility of oceanic jet formation and the resultant effect upon the sea ice edge is less conclusive. Observations and climate model data of the polar oceans have been analyzed to show areas of likely atmospheric jet formation, with the Fram Strait being of particular interest.

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill.

An AeroCom assessment of black carbon in Arctic snow and sea ice

Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. Here, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during which an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are −4.4 (−13.2 to +10.7) ng g−1 for an earlier phase of AeroCom models (phase I), and +4.1 (−13.0 to +21.4) ng g−1 for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g−1. Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model–measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60–90° N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07–0.25) W m−2 and 0.18 (0.06–0.28) W m−2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m−2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.

Atmospheric response in summer linked to recent Arctic sea ice loss

Since 2007 a large decline in Arctic sea ice has been observed. The large-scale atmospheric circulation response to this decline is investigated in ERA-Interim reanalyses and HadGEM3 climate model experiments. In winter, post-2007 observed circulation anomalies over the Arctic, North Atlantic and Eurasia are small compared to interannual variability. In summer, the post-2007 observed circulation is dominated by an anticyclonic anomaly over Greenland which has a large signal-to-noise ratio. Climate model experiments driven by observed SST and sea ice anomalies are able to capture the summertime pattern of observed circulation anomalies, although the magnitude is a third of that observed. The experiments suggest high SSTs and reduced sea ice in the Labrador Sea lead to positive temperature anomalies in the lower troposphere which weaken the westerlies over North America through thermal wind balance. The experiments also capture cyclonic anomalies over Northwest Europe, which are consistent with downstream Rossby wave propagation